diff --git a/docs/examples/topic_bounding.ipynb b/docs/examples/topic_bounding.ipynb new file mode 100644 index 00000000..401bed9a --- /dev/null +++ b/docs/examples/topic_bounding.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bound a topic by a 1D or 2D Manifold Projection\n", + "\n", + "Here's the main idea:\n", + "\n", + "1) A sentence embedding gives you a map or function between the set of tokens (including position) into a fixed point in semantic space.\n", + "2) The distances in the semantic space can be non linear so we use a manifold projection tool like u-map to project the embedding points down to 1D or 2D.\n", + "3) If you have a set of examples of different topics they map to different areas in 1D or 2D.\n", + "4) Fit a projection on a whole host of conversations and topics, check that there is a good separation of the topics in the lower dimension.\n", + "5) You can use a convex-hull in 2d, a simple bin, or even a probability test to determine if a new sentence falls within a topic or not.\n", + "6) You can quickly check if the input or output to an LLM is a space, if not guide it to get back on topic" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "import pandas as pd\n", + "\n", + "wiki_topics = load_dataset(\"tarekziade/wikipedia-topics\")\n", + "\n", + "df = wiki_topics['test'].to_pandas().iloc[:100]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "from semantic_router.encoders import FastEmbedEncoder\n", + "\n", + "encoder = FastEmbedEncoder(name=\"BAAI/bge-small-en-v1.5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_list = encoder(df['text'].to_list())" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "100" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(embedding_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "import umap\n", + "reducer = umap.UMAP(n_components=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "embedding = reducer.fit_transform(embedding_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.assign(E=[float(_emb[0].astype(float)) for _emb in embedding])" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "df['categories'] = df['categories'].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([\"['Ethics']\", \"['Mass_media']\", \"['Entities']\",\n", + " \"['Food_and_drink']\", \"['Technology']\", \"['Education']\",\n", + " \"['Health']\", \"['Energy']\", \"['Government']\", \"['Knowledge']\",\n", + " \"['Information']\", \"['Humanities']\", \"['Religion']\", \"['Nature']\",\n", + " \"['Philosophy']\", \"['Economy']\", \"['Time']\", \"['Human_behavior']\",\n", + " \"['Engineering']\", \"['Military']\", \"['Law']\", \"['Politics']\",\n", + " \"['History']\", \"['Education' 'Academic_disciplines']\",\n", + " \"['Society' 'Culture']\", \"['Geography']\", \"['Sports']\",\n", + " \"['Language']\", \"['Lists']\", \"['Life']\", \"['Internet']\",\n", + " \"['Communication']\", \"['Universe' 'Mathematics']\",\n", + " \"['Knowledge' 'Concepts']\", \"['Entertainment']\", \"['Universe']\",\n", + " \"['Science']\", \"['People']\"], dtype=object)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['categories'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the list of values to filter\n", + "value_list = [\"['Religion']\", \"['Technology']\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "filtered_df = df[df['categories'].isin(value_list)]" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idtitlesummarytextcategoriesE
42457647National Computing CentreThe National Computing Centre (NCC) was an ind...The National Computing Centre (NCC) was an ind...['Technology']6.547527
928196618Margaret Hamilton (software engineer)Margaret Elaine Hamilton (née Heafield; born A...Margaret Elaine Hamilton (née Heafield; born A...['Technology']6.512669
1568219812St. Mary's Church, KilkennySt. Mary's Church was a church in Kilkenny, Ir...St. Mary's Church was a church in Kilkenny, Ir...['Religion']1.570939
2046773775St. Wenceslas Church (Zderaz)St. Wenceslas Church at Zderaz (Czech: Kostel ...St. Wenceslas Church at Zderaz (Czech: Kostel ...['Religion']1.720816
251127001PC PowerPlayPC PowerPlay (PCPP) is Australia's only dedica...PC PowerPlay (PCPP) is Australia's only dedica...['Technology']4.054253
3149675317Murray ShanahanMurray Patrick Shanahan is a professor of Cogn...Murray Patrick Shanahan is a professor of Cogn...['Technology']6.417150
6040642665St. Augustine's Church, CopenhagenSt. Augustine's Church is a Roman-Catholic chu...St. Augustine's Church is a Roman-Catholic chu...['Religion']1.896111
6652519539Samantha JohnSamantha John (born 1985 or 1986 (age 37–38)) ...Samantha John (born 1985 or 1986 (age 37–38)) ...['Technology']6.268058
8055588627May (painting)The Mays were a series of paintings in 17th an...The Mays were a series of paintings in 17th an...['Religion']2.094458
\n", + "
" + ], + "text/plain": [ + " id title \\\n", + "4 2457647 National Computing Centre \n", + "9 28196618 Margaret Hamilton (software engineer) \n", + "15 68219812 St. Mary's Church, Kilkenny \n", + "20 46773775 St. Wenceslas Church (Zderaz) \n", + "25 1127001 PC PowerPlay \n", + "31 49675317 Murray Shanahan \n", + "60 40642665 St. Augustine's Church, Copenhagen \n", + "66 52519539 Samantha John \n", + "80 55588627 May (painting) \n", + "\n", + " summary \\\n", + "4 The National Computing Centre (NCC) was an ind... \n", + "9 Margaret Elaine Hamilton (née Heafield; born A... \n", + "15 St. Mary's Church was a church in Kilkenny, Ir... \n", + "20 St. Wenceslas Church at Zderaz (Czech: Kostel ... \n", + "25 PC PowerPlay (PCPP) is Australia's only dedica... \n", + "31 Murray Patrick Shanahan is a professor of Cogn... \n", + "60 St. Augustine's Church is a Roman-Catholic chu... \n", + "66 Samantha John (born 1985 or 1986 (age 37–38)) ... \n", + "80 The Mays were a series of paintings in 17th an... \n", + "\n", + " text categories \\\n", + "4 The National Computing Centre (NCC) was an ind... ['Technology'] \n", + "9 Margaret Elaine Hamilton (née Heafield; born A... ['Technology'] \n", + "15 St. Mary's Church was a church in Kilkenny, Ir... ['Religion'] \n", + "20 St. Wenceslas Church at Zderaz (Czech: Kostel ... ['Religion'] \n", + "25 PC PowerPlay (PCPP) is Australia's only dedica... ['Technology'] \n", + "31 Murray Patrick Shanahan is a professor of Cogn... ['Technology'] \n", + "60 St. Augustine's Church is a Roman-Catholic chu... ['Religion'] \n", + "66 Samantha John (born 1985 or 1986 (age 37–38)) ... ['Technology'] \n", + "80 The Mays were a series of paintings in 17th an... ['Religion'] \n", + "\n", + " E \n", + "4 6.547527 \n", + "9 6.512669 \n", + "15 1.570939 \n", + "20 1.720816 \n", + "25 4.054253 \n", + "31 6.417150 \n", + "60 1.896111 \n", + "66 6.268058 \n", + "80 2.094458 " + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_df" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "sns.kdeplot(data=filtered_df, x='E', hue = 'categories', fill=True, common_norm=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}